My research also extends to the application of **machine learning (ML)** techniques to enhance both of my primary research fields. For the solar panel feasibility study, I use ML models to predict energy generation based on weather patterns and environmental data.
In neutrino analysis, I develop ML algorithms to more efficiently identify and classify neutrino events within the vast datasets collected by IceCube. This approach helps to filter out background noise and focus on the most scientifically valuable events.
By leveraging the power of machine learning, I aim to accelerate discovery and unlock new insights from complex physical systems.